ALBERT4Rec: Sequential Recommendation with A Lite Bidirectional Encoder Representations from Transformer

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研究生：	劉信佑 Xin-You Liu
論文名稱：	ALBERT4Rec: Sequential Recommendation with A Lite Bidirectional Encoder Representations from Transformer ALBERT4Rec: Sequential Recommendation with A Lite Bidirectional Encoder Representations from Transformer
指導教授：	吳怡樂 Yi-Leh Wu
口試委員:	唐政元 Zheng-Yuan Tang 陳建中 Jian-Zhong Chen 閻立剛 Li-Gang Yan
學位類別：	碩士 Master
系所名稱：	電資學院 - 資訊工程系 Department of Computer Science and Information Engineering
論文出版年：	2020
畢業學年度：	109
語文別：	英文
論文頁數：	30
中文關鍵詞：	推薦系統、深度學習、雙向序列模型、多頭自我注意力機制、轉譯器的雙向編碼表示、跨層參數共享
外文關鍵詞：	Recommendation System, Deep Learning, Bidirectional Sequential Model, Multi-Head Self-Attention Mechanism, BERT, ALBERT
相關次數：	點閱：328 下載：24
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上一筆

推薦系統已經成為現今商業網站不可或缺的一部分，如何有效地推薦使用者感興趣的事物，是現今推薦系統的研究者持續努力的方向。推薦系統的資料來源通常是來自於使用者與物品之間的交互行為，例如評分、點擊、瀏覽紀錄等等，我們的目的是透過使用者的行為來預測下一個時刻的物品。近年來，有研究者提出了基於BERT（“轉譯器的雙向編碼表示＂的縮寫）的BERT4Rec推薦模型並刷新了效能紀錄。由於BERT4Rec的啟發，我們找到了BERT的下一代版本ALBERT（“輕量轉譯器的雙向編碼表示＂的縮寫），並將他實作成序列推薦系統，期望獲得更佳的性能。實驗中，我們採用真實世界的資料集:MovieLens來加以實驗，這個資料集被廣泛地用來評估一個推薦系統的性能好壞。此外，我們還分析了嵌入層的維度大小、隱藏層的維度大小、多頭自我注意力機制、跨層參數共享、遮罩比例以及序列最大長度的影響，並且以最佳參數調整模型，達到了最佳的性能。實驗結果證明，使用我們提出的ALBERT4Rec的性能比BERT4Rec更好，效能提升了約20%。

Recommendation systems have become an indispensable part of today's commercial websites. How to effectively recommend things that users are interested in is the direction that researchers of current recommendation systems continue to work hard on. The data source of the recommendation system usually comes from the interaction between the users and the items, such as ratings, clicks, browsing histories, etc. Our purpose is to predict the next item through the user's behavior. In recent years, some researchers have proposed the BERT4Rec recommendation model which is based on Bidirectional Encoder Representations from Transformer (BERT) and has achieved state of the art. Inspired by the BERT4Rec, we found the next-generation version of the BERT, A Lite Bidirectional Encoder Representations from Transformer (ALBERT), and implement it as a sequential recommendation system, hoping to obtain better performance. In the experiment, we use a real-world data set: MovieLens to experiment, this data set is widely used to evaluate the performance of a recommendation system. Furthermore, we also analyze the dimensionality of the embedding layer, the dimensionality of the hidden layer, the multi-head self-attention mechanism, the cross-layer parameter sharing, the masked proportion and the maximum length of the sequence, then use the best parameter fine tune model to achieve best performance. Experimental results show that the performance of using our proposed ALBERT4Rec is better than the BERT4Rec, and the performance is increased by about 20%.

論文摘要    ii
Abstract    iii
Contents    iv
LIST OF FIGURES    iv
LIST OF TABLES    v
Chapter 1. Introduction    1
1.1 Research Background    1
1.2 Research Motivation    2
Chapter 2. Related Work    3
2.1 General Recommendation    3
2.2 Sequential Recommendation    3
2.3 Attention Mechanism    4
Chapter 3. Proposed Method    8
3.1 Problem Statement    8
Table 3.1: Notation.    8
3.2 Model Architecture    8
3.3 Embedding Layer    9
3.4 Transformer Layer    10
3.5 Output Layer    12
3.6 Model Learning    12
Chapter 4. Experiments    14
4.1 Datasets    14
Table 4.1: Statistics of the datasets.    14
4.2 Task Settings & Evaluation Metrics    14
4.3 Baselines & Implementation Details    15
Table 4.2: Parameters setting for training model.    15
4.4 Overall Performance Comparison    16
Table 4.3: Overall performance. In each row, the best score is boldfaced, and the second-best score is underlined. Improvements over the ALBERT4Rec are shown in the last column.    16
4.5 Performance Comparison in Different Argument    17
Table 4.4: Performance comparison in different argument.    17
4.6 The Impact of Embedded Size    17
4.7 The Impact of Hidden Units    18
4.7 The Impact of Multi-Head Self-Attention    18
4.8 The Impact of Cross-layer Parameter Sharing    19
4.9 The Impact of Masked Proportion    19
4.10 The Impact of Max Length    20
Chapter 5. Conclusions and Future Work    21
References    22

                                

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